insaight
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- License — License: MIT
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- network request — Outbound network request in insaight/mcp_server.py
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Bu listing icin henuz AI raporu yok.
LinkedIn prospect intelligence inside Claude — MCP server + 8 skills that research people, companies and comment threads, draft outreach, and learn what gets replies.
insaight
LinkedIn prospect intelligence inside Claude — it automates the research, not the outreach.

Insaight scrapes public LinkedIn data via Apify, stores it in local SQLite, and hands it to Claude through an MCP server and eight skills. Data flows in once, then stays on your machine — repeat questions hit SQLite, not Apify.
Architecture
graph LR
C["Claude Code / Desktop<br/>8 skills"] -->|MCP| S["insaight server<br/>18 tools"]
S --> DB[("SQLite<br/>~/.insaight/posts.db")]
S --> M["memory/<br/>style.md · playbook.md"]
S -->|fresh scrapes only| A["Apify<br/>LinkedIn actors"]
Install in 30 seconds
Prerequisite: uv and Python 3.11+. The plugin runs the MCP server with uvx, so without uv the tools never load.
curl -LsSf https://astral.sh/uv/install.sh | sh # skip if you already have uv
Then, in Claude Code:
/plugin marketplace add spirosbax/insaight
/plugin install insaight@insaight
Add your Apify token (free tier works):
mkdir -p ~/.insaight && echo "APIFY_API_TOKEN=apify_api_..." >> ~/.insaight/.env
Restart Claude Code and say "research Anthropic on LinkedIn". The plugin registers the MCP server and installs all eight skills; there is nothing to clone.
Claude DesktopOn the very first run,
uvxbuilds the server before it answers — give it a few seconds. If the insaight tools never appear, check thatuvis on yourPATH.
Add to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):
{
"mcpServers": {
"insaight": {
"command": "uvx",
"args": ["--from", "git+https://github.com/spirosbax/insaight", "insaight"],
"env": { "APIFY_API_TOKEN": "apify_api_..." }
}
}
}
Restart Claude Desktop, then add the skills under Settings → Skills → Add skills, selecting the SKILL.md files from this repo's skills/ directory.
git clone https://github.com/spirosbax/insaight.git && cd insaight
uv venv && source .venv/bin/activate
uv pip install -e ".[dev]"
pytest -q # hermetic — temp SQLite, no credentials
claude mcp add insaight -s user -- "$PWD/.venv/bin/insaight" # local checkout instead of uvx
A checkout with a data/ directory uses it as INSAIGHT_HOME, keeping the dev database inside the repo (gitignored).
Skills
Eight skills that chain conversationally — each one's output feeds the next. They are plain Markdown with YAML frontmatter: easy to read, fork, and customize.
| Skill | One line |
|---|---|
| research-person | Intelligence brief on an individual: themes, decision-maker signals, outreach hooks, uncommon commonalities |
| research-company | Company analysis from company posts + up to 3 C-level executives' posts, with a prospect score |
| research-post | Mine a post's comment thread for warm leads, decision-makers, and competitor mentions |
| draft-outreach | Cold DM + email, two variants each, using prior research + your learned style memory |
| draft-post | LinkedIn post in your company's voice, styled on your own past posts (URL-to-post supported) |
| track-outreach | Log sends and outcomes in the local ledger ("I sent it", "she replied", "mark as ghosted") |
| reflect | Analyze outcomes, propose evidence-backed memory updates — applied only on your approval |
| save-notion | Persist research briefs to your configured Notion page (optional, needs the Notion MCP) |
prospecting research company → draft outreach → save to Notion
person-first research person → draft outreach
qualification research company → read the prospect evaluation → pursue or pass
What it actually looks like in the terminal

An unedited Claude Code session: install, research Anthropic, find the right person, draft the DM, log the send.
The memory loop
draft → send → "I sent it" → logged (log_outreach)
→ "she replied" / "ghosted" → outcome recorded (record_outcome)
→ every N outcomes → reflection proposed (default 10; REFLECT_EVERY)
→ you approve → style.md + playbook.md updated
Outcomes are logged because you say so — Insaight never reads your inbox. Every playbook claim carries its evidence ("question hooks: 4/9 replied vs statement hooks: 1/8"), and below n=10 a pattern is a hypothesis, not a rule. Nothing is written to memory without your approval. The ledger also powers prior-contact warnings ("you messaged this person 3 weeks ago — ghosted") whenever you research or draft.
MCP tool reference (18 tools)| Tool | Purpose |
|---|---|
list_accounts |
Discover tracked companies and personal profiles |
scrape_profile |
Fetch fresh posts for any LinkedIn URL (Apify) |
scrape_people |
Fetch company employees and leadership (Apify, Short or Full mode) |
scrape_person_profile |
Enrich one person with full profile: experience, education, skills, volunteer, languages |
list_posts |
Token-cheap index: metadata + 150-char snippet |
get_posts |
Full content for selected posts by URN (max 20 per call) |
search_posts |
Full-text keyword search across stored posts |
list_people |
Query stored employees/leadership (instant, free) |
scrape_post_comments |
Fetch a post's comment thread with author info (Apify) |
list_comments |
Query stored comments for a post, ranked by likes |
get_stats |
Database overview: counts, date range, categories |
log_outreach |
Record a sent message in the ledger (flags prior contact) |
record_outcome |
Record replied / positive / meeting / ghosted; flags when reflection is due |
list_outreach |
Query the ledger: prior-contact checks, pending sends, history |
get_outreach_stats |
Reply-rate breakdown by hook type, variant, and channel |
get_memory |
Read the learned style guide + strategy playbook |
update_memory |
Rewrite a memory file (only after an approved reflection) |
get_config |
Read your Notion pages + company config from ~/.insaight/config.md (creates it with placeholders on first call) |
Reading pattern: list_posts returns ~80 tokens per post; scan snippets, then get_posts only the interesting ones.
Everything is under ~/.insaight/ (override with INSAIGHT_HOME):
~/.insaight/
.env APIFY_API_TOKEN, ANTHROPIC_API_KEY (optional), REFLECT_EVERY
config.md Notion pages + company config (read by get_config)
posts.db SQLite: posts, people, comments, outreach ledger
memory/ style.md + playbook.md (written by the reflect skill)
Apify actors & costs
| Actor | Scrapes | Approx. cost |
|---|---|---|
harvestapi/linkedin-profile-posts |
Company or personal posts | ~$1.50 / 1k posts |
harvestapi/linkedin-company-employees |
Employees and leadership | ~$4 / 1k (Short), ~$8 / 1k (Full) |
harvestapi/linkedin-profile-scraper |
Single-profile enrichment | $4 / 1k ($10 / 1k with email search) |
harvestapi/linkedin-post-comments |
Comment threads | see actor page |
Rates as published at time of writing — check the actor pages for current pricing.
CLIA standalone CLI for batch work outside Claude (insaight-cli in a dev install, or uvx --from git+https://github.com/spirosbax/insaight insaight-cli):
insaight-cli scrape --accounts config/accounts.txt # scrape tracked accounts (--no-categorize skips the Anthropic API)
insaight-cli stats # database overview
insaight-cli export --format csv --output posts.csv # export to CSV or JSON
Data, privacy & terms
Everything stays local: posts, people, the outreach ledger, and learned memory live in SQLite and Markdown on your machine, and nothing is sent anywhere except your own Apify/Anthropic/Notion accounts. No inbox access — outcomes exist because you reported them. Insaight fetches public LinkedIn data through third-party Apify actors; automated collection may conflict with LinkedIn's Terms of Service, and you are responsible for how you use this tool. Keep volumes reasonable and respect the people behind the profiles.
License
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